Evaluation of an artificial intelligence model for the identification of obstructive hydrocephalus on computed tomography of the head.
Authors
Affiliations (6)
Affiliations (6)
- Digital CRO, Mass General Brigham AI, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
- Digital CRO, Mass General Brigham AI, Boston, MA, USA. [email protected].
- Harvard Medical School, Boston, MA, USA. [email protected].
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA. [email protected].
Abstract
Obstructive hydrocephalus is a critical radiographic finding requiring emergent treatment. Its identification on head CT by an AI model could facilitate sooner life-saving interventions, although there are common co-occurring findings, including intracranial hemorrhage, that can confound this interpretation. This external validation assessed the accuracy of an AI model at identifying obstructive hydrocephalus, including in the presence or absence of other findings. This retrospective cohort included 177 thin (≤ 1.5 mm) series and 194 thick (> 1.5 and ≤ 5 mm) series from 200 non-contrast head CT cases. These cases were obtained from patients aged ≥ 18 years at 5 hospitals in the United States. Each case was interpreted independently by up to three neuroradiologists. Each series was then interpreted by the AI model. The AI model performed with an area under the curve of 0.988 (95% confidence interval (CI): 0.971-0.998) on thin series and 0.986 (95% CI: 0.969-0.997) on thick series. These results were broadly maintained in subgroups for the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain, and across demographic and scanner manufacturer subgroups. The AI model accurately identified obstructive hydrocephalus in this dataset. Its performance in subgroup analyses reflected its robustness. Question Can an artificial intelligence model accurately identify obstructive hydrocephalus on head computed tomography, including in the presence or absence of common co-occurring imaging findings? Findings This model accurately identified obstructive hydrocephalus on thin and thick series, including in the presence or absence of intracranial hemorrhage, parenchymal abnormality, and ventricular drain. Clinical relevance This model could assist with triaging abnormal cases, enabling earlier identification and management of obstructive hydrocephalus. Its maintained performance with or without co-occurring findings suggests it specifically identifies obstructive hydrocephalus rather than these findings.